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[2602.13791] MechPert: Mechanistic Consensus as an Inductive Bias for Unseen Perturbation Prediction
Summary
The paper introduces MechPert, a framework that enhances unseen genetic perturbation prediction by leveraging mechanistic consensus among multiple agents, improving prediction accuracy significantly over existing methods.
Why It Matters
Understanding gene regulation through accurate prediction of transcriptional responses to genetic perturbations is crucial for biological research and experimental design. MechPert's innovative approach addresses limitations in current models, potentially leading to more effective experimental strategies and insights in genetics.
Key Takeaways
- MechPert improves prediction accuracy for unseen genetic perturbations by up to 10.5%.
- The framework utilizes multiple agents to propose regulatory hypotheses, enhancing robustness.
- MechPert outperforms traditional methods in experimental design by selecting more effective anchor genes.
Computer Science > Machine Learning arXiv:2602.13791 (cs) [Submitted on 14 Feb 2026] Title:MechPert: Mechanistic Consensus as an Inductive Bias for Unseen Perturbation Prediction Authors:Marc Boubnovski Martell, Josefa Lia Stoisser, Lawrence Phillips, Aditya Misra, Robert Kitchen, Jesper Ferkinghoff-Borg, Jialin Yu, Philip Torr, Kaspar Märten View a PDF of the paper titled MechPert: Mechanistic Consensus as an Inductive Bias for Unseen Perturbation Prediction, by Marc Boubnovski Martell and 8 other authors View PDF HTML (experimental) Abstract:Predicting transcriptional responses to unseen genetic perturbations is essential for understanding gene regulation and prioritizing large-scale perturbation experiments. Existing approaches either rely on static, potentially incomplete knowledge graphs, or prompt language models for functionally similar genes, retrieving associations shaped by symmetric co-occurrence in scientific text rather than directed regulatory logic. We introduce MechPert, a lightweight framework that encourages LLM agents to generate directed regulatory hypotheses rather than relying solely on functional similarity. Multiple agents independently propose candidate regulators with associated confidence scores; these are aggregated through a consensus mechanism that filters spurious associations, producing weighted neighborhoods for downstream prediction. We evaluate MechPert on Perturb-seq benchmarks across four human cell lines. For perturbation prediction in...